def main(train_file='train.csv', test_file='test.csv', output_file='predict.csv'):
print "Loading data..."
train_data = pd.read_csv(train_file)
test_data = pd.read_csv(test_file)
y = np.array(train_data[["ACTION"]])
#X = np.array(train_data.ix[:,1:-1]) # Ignores ACTION, ROLE_CODE
X = np.array(train_data[["RESOURCE","MGR_ID", "ROLE_ROLLUP_1", "ROLE_ROLLUP_2", "ROLE_DEPTNAME", "ROLE_FAMILY_DESC", "ROLE_FAMILY", "ROLE_DEPTNAME", "ROLE_CODE"]])
X_test = np.array(test_data[["RESOURCE","MGR_ID", "ROLE_ROLLUP_1", "ROLE_ROLLUP_2", "ROLE_DEPTNAME", "ROLE_FAMILY_DESC", "ROLE_FAMILY","ROLE_DEPTNAME", "ROLE_CODE"]]) # Ignores ID, ROLE_CODE
SEED = 4
#clf = DecisionTreeClassifier(criterion="entropy").fit(X,y)
clf = RandomForestRegressor(n_estimators=300, min_samples_split=15, min_density=0.1,compute_importances=True).fit(X,y)
print clf.feature_importances_
#Try feature selection
mean_auc = 0.0
n = 10
for i in range(n):
X_train, X_cv, y_train, y_cv = cross_validation.train_test_split(X, y, test_size=.10, random_state=i*SEED)
# if you want to perform feature selection / hyperparameter
# optimization, this is where you want to do it
# train model and make predictions
clf.fit(X_train, y_train)
preds = clf.predict(X_cv)
# compute AUC metric for this CV fold
fpr, tpr, thresholds = metrics.roc_curve(y_cv, preds, pos_label=1)
roc_auc = metrics.auc(fpr, tpr)
print "AUC (fold %d/%d): %f" % (i + 1, n, roc_auc)
mean_auc += roc_auc
print "Mean AUC: %f" % (mean_auc/n)
predictions = clf.predict_(X_test)
#print predictions
#print 'Writing predictions to %s...' % (output_file)
create_test_submission(output_file, predictions)
return 0
